000 02228nam a22003498i 4500
001 CR9780511611483
003 UkCbUP
005 20200124160220.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 090910s2008||||enk o ||1 0|eng|d
020 _a9780511611483 (ebook)
020 _z9780521865067 (hardback)
020 _z9780521683579 (paperback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aQA279
_b.B35 2008
082 0 0 _a519.57
_222
100 1 _aBailey, R.
_q(Rosemary),
_eauthor.
245 1 0 _aDesign of comparative experiments /
_cR.A. Bailey.
264 1 _aCambridge :
_bCambridge University Press,
_c2008.
300 _a1 online resource (xiii, 330 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aCambridge series on statistical and probabilistic mathematics ;
_v25
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aThis book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.
650 0 _aExperimental design.
776 0 8 _iPrint version:
_z9780521865067
830 0 _aCambridge series on statistical and probabilistic mathematics ;
_v25.
856 4 0 _uhttps://doi.org/10.1017/CBO9780511611483
999 _c516673
_d516671